1. Technical Field
The present disclosure relates generally to automated determination of lymph nodes in non-invasive scanned images of a body, such as in X-ray computed tomography (CT) scans and magnetic resonance imagery (MRI) scans of a human body.
2. Background
One way that tumors spread is through a patient's lymphatic system. Lymph node metastasis is a significant prognostic factor in many types of primary solid and hematologic malignancies. A patient's appropriate staging for either surgical or medical therapy depends upon the recognition and proper evaluation of lymph nodes throughout the body. A patient's disease prognosis also depends on the extent of lymph node involvement with a tumor at the time of diagnosis. After a patient has been treated with either surgery, radiation therapy or medical (chemotherapy) therapy, evaluation of these lymph nodes for decrease in size and number (response to therapy) or increase in size and number (progression or worsening of disease) is critical for judging the success or failure of a therapy and whether to continue with a successful therapy or change to a different therapy because of treatment failure. Determination of extent and size change of the lymph nodes on longitudinal CT scans obtained at different times during the course of a patient's active treatment and follow up after active treatment plays an important role throughout the practice of oncology.
The detection of lymph nodes, especially small lymph nodes, is difficult and tedious for a radiologist, because lymph nodes are “intertwined” with normal anatomic structures such as blood vessels and the bowel. Detection of these lymph nodes is critical for proper staging of a patient's tumor. If a lymph node is missed, i.e., not identified, a patient may be deemed mistakenly to have a local tumor which has not spread. This can result in inappropriate treatment for their cancer, for instance, by subjecting the patient to unnecessary surgery that would not have been performed if it had been recognized that the tumor had involved a lymph node. Identification of a structure as a lymph node which is not, could have similar dire consequences. For example, the patient can be deemed to have diffuse disease, for which surgery is not appropriate, when they may in fact have local disease for which surgery is the correct treatment. Lacking correct treatment, the patient's cancer may spread unnecessarily and threaten the patient's health and life.
Measurement of lymph nodes on medical imaging is one of the principal ways of determining if a lymph node may be involved with tumor. There are anatomically and pathologically defined cut off values for determining if a lymph node may have tumor in it or not. In general the larger a lymph node, the more suspicious it is for having tumor. The cut off values may be determined by individual lymph node region or group with an appropriately acceptable sensitivity and specificity. Evaluation of response to either a chemotherapy, surgery or radiation therapy is critical for determining if a treatment (either conventional or experimental) is efficient or if an experimental agent is effective against a specific tumor type.
For the past three decades, the standard way to assess response of tumors to therapy has been to use radiographic images, principally CT or magnetic resonance imaging (MRI), to measure tumor size using the bi-dimensional World Health Organization (WHO) criteria or uni-dimensional RECIST criteria. The bi-directional WHO criteria is based upon a temporal change in the sum of the target tumor size on multiple transverse images, in which tumor size on a transverse image is measured as the cross product of the two greatest perpendicular diameters. A transverse image is one that represents a body's internal tissues in a plane perpendicular to an axial direction; and the axial direction is parallel to the body's long axis, such as the direction from the top of a typical human body's head to the bottom of that body's feet. The uni-dimensional RECIST criteria is based upon a temporal change in the sum of the target tumor sizes on multiple transverse images, in which tumor size on a transverse image is measured by the tumor's maximum diameter.
However, the measurement of tumors on medical imaging is a manual and subjective task performed by a trained radiologist according to current clinical practice. Studies have shown significant variability in therapeutic response assessment. Reasons for the variability in response assessment include radiologists' intra- and inter-observer variability, imaging protocols (e.g., phase of intravenous contrast administration and image resolution) and measurement techniques used (e.g., hand-held caliper, electronic caliper, and semi-automated/automated technique. (See for example, Thiesse P, Ollivier L, Di Stefano-Louineau D, et al. “Response rate accuracy in oncology trials: Reasons for inter-observer variability,” J Clin Oncol. 1997; vol. 15, pp 3507-3514.)
State-of-the-art medical imaging modalities in combination with advanced image processing algorithms are revolutionizing the traditional diagnostic methods in radiology and oncology. Older model CT scanners produced transverse plane images (called slices) with two dimensional picture elements (called pixels) at positions with substantial gaps in the axial direction, leading to partial volume artifacts, such as poor descriptions of axial edges. It is now technically feasible with multi-detector-row CT scanners to acquire images with isotropic three dimensional picture elements (called volume elements, or voxels) with sub-millimeter resolution along the perpendicular x, y directions in the transverse plane and in the axial (z) direction. Typically, the x direction is from the body's left side to the body's right side in the transverse plane; and, the y direction is from the body's back (posterior) to the body's front (anterior). This has reduced the partial volume artifacts associated with more traditional CT images; and true tumor volumes can now be measured with a high degree of accuracy. Asymmetric changes, particularly those along the axial direction, can be better detected with volumetric rather than with uni-dimensional or bi-dimensional measurements. The volumetric methods may help detect metastasis early and provide earlier and more accurate assessment of response to therapy.
While methods have been developed to assist the radiologist in finding tumors, to applicants' knowledge no methods are currently available that automatically determine lymph nodes in non-invasive scanned images without human intervention. This is because there are significant challenges in assessing multiple sites of lymph nodes throughout the body, and different surrounding structures with varying intensity contrasts to the lymph nodes.
Some investigators evaluated several standard techniques using images of phantoms and enhanced lymph nodes in rabbits (see, Rogowska J, Ketth B, Scott G G et al., “Evaluation of selected two-dimensional segmentation techniques for computed tomography quantization of lymph nodes,” Investigative Radiology, 1996, vol. 31, pp 138-145, hereinafter Rogowska). Rogowska found that a Sobel/watershed technique and an interactive deformable contour algorithm had advantages over the other techniques with regard to user interaction, reproducibility and accuracy. However, results assume manual input of internal and external markers by a human to initialize the methods.
Others attempted to semi-automatically segment lymph nodes using a two-dimensional (2-D) and then a three-dimensional (3-D) active contour method. (See Honea D M, Ge Y, Snyder W E, et al., “Lymph node segmentation using active contours”, Proc. SPIE, 1997, vol. 3034, pp 265-273; and Honea D M, and Snyder W E, “Three-Dimensional active surface approach to lymph node segmentation”, Proc. SPIE, 1999; vol. 3661, pp 1003-1011, both referenced hereinafter as Honea). The Honea algorithms required well-defined edges and high similarity of the node boundaries between adjacent slices. These conditions can not be satisfied in may scanned images.
Based on region intensity features and a fast marching method, still others proposed an improved fast marching algorithm to segment lymph nodes. (See Yan J, Zhuang T, Zhao B, Schwartz L H, “Lymph node segmentation from CT images using fast marching method,” Computerized Medical Imaging and Graphics, 2004, vol. 28, pp 33-38, hereinafter Yan.) The Yan algorithm was effective when the nodes had relatively homogenous intensities. However, the Yan results required a manually drawn initial circle to be as close as possible to the node boundary, a requirement which might not be satisfied when automatically segmenting sequential images.
Accordingly there is a need for an automated or semi-automated system that can objectively and accurately detect or measure lymph nodes or assess change in lymph node size on temporally separated CT scans in clinical practice, which does not suffer the disadvantages of prior art approaches.